KAUST and the National Center for Palm and Dates (NCPD) have entered a SR100 million agreement for research projects in the date palm sector. The agreement aims to improve production efficiency, develop innovative agricultural practices, and mitigate economic risks to palm trees. KAUST will leverage its expertise to create a genetic atlas for Saudi date varieties and adopt sustainable management practices. Why it matters: This investment highlights Saudi Arabia's commitment to its cultural heritage and economic diversification through advancements in a key agricultural sector.
KAUST researchers are undertaking a project to improve global date palm production and protection by studying the date palm genome, collecting samples from ancient palms near Madinah. They aim to develop new breeding strategies for faster, healthier, and more pest-resistant palms. The research involves advanced genome sequencing and the creation of molecular tools to improve date palm agriculture, including rapid sex determination methods and gene editing. Why it matters: This research is critical for enhancing date production in arid regions like Saudi Arabia, which is a major global producer, and for ensuring food security amidst climate challenges.
This paper proposes a machine learning method for early detection and classification of date fruit diseases, which are economically important to countries like Saudi Arabia. The method uses a hybrid feature extraction approach combining L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features. Experiments using a dataset of 871 images achieved the highest average accuracy using Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT) classifiers.
KAUST researchers are collaborating with the Saudi Ministry of Environment, Water & Agriculture (MEWA) to develop sensor technology for early detection of red palm weevils. The weevil larvae cause significant damage to palm trees by hollowing them out from the inside. Early detection is crucial because visible signs of distress indicate advanced infection and low chances of rescue. Why it matters: This research aims to protect date farming and crops, which are a vital economic resource for Saudi Arabia and the broader region.
KAUST is developing a robotic system for automated date palm harvesting, combining robotics and AI. The system uses robotic arms with visual sensors to identify and harvest dates, flowers, and tree structures. Field trials are scheduled for the 2025 harvest season, with full operational capability expected within three years. Why it matters: This innovation could transform Saudi Arabia's date farming industry, increasing yields, reducing labor risks, and positioning the country as a leader in agricultural technology.
A new culturally inclusive and linguistically diverse dataset called Palm for Arabic LLMs is introduced, covering 22 Arab countries and featuring instructions in both Modern Standard Arabic (MSA) and dialectal Arabic (DA) across 20 topics. The dataset was built through a year-long community-driven project involving 44 researchers from across the Arab world. Evaluation of frontier LLMs using the dataset reveals limitations in cultural and dialectal understanding, with some countries being better represented than others.
KAUST researchers Dr. Islam Ashry, Dr. Chun Hong Kang, and Professor Boon S. Ooi won the 2022 International Date Palm Innovative Technology Excellence Prize from the National Center for Palm and Dates (NCPD) for their fiber-optic distributed acoustic sensor. The sensor enables early detection of red palm weevils in date palm trees, a major problem in the Middle East, Southeast Asia, and Europe. The prize includes a SAR 200,000 reward. Why it matters: This award recognizes innovative research addressing a critical agricultural challenge in the region, with potential for significant economic impact by reducing losses from infestations.
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.